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International Journal of Frontiers in Engineering Technology, 2023, 5(12); doi: 10.25236/IJFET.2023.051214.

Study on Deep Learning of Aircraft Safety Enhancement and Autonomous Flight Assistance


Ruixiang Luo, Xiangqi Chen

Corresponding Author:
Ruixiang Luo

School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510006, China


Flight safety has always been an important concern in the aviation field. This study proposes a comprehensive scheme for aircraft assisted piloting based on deep learning. This solution uses the LSTM network for real-time aircraft status monitoring and error correction prompts, as well as aircraft autopilot assistance functions. At the same time, the database is updated through the real-time flight data processing module. The comprehensive program is designed to improve flight safety and pilot decision-making. Through automated early warning, error correction and assisted driving, real-time flight advice and control parameters are provided to improve flight safety and pilot operating capabilities.


Neural Network, SEGA, LSTM, Aircraft Auxiliary Driving

Cite This Paper

Ruixiang Luo, Xiangqi Chen. Study on Deep Learning of Aircraft Safety Enhancement and Autonomous Flight Assistance. International Journal of Frontiers in Engineering Technology (2023), Vol. 5, Issue 12: 88-96. https://doi.org/10.25236/IJFET.2023.051214.


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